Data‐Driven Pathways to Circular E‐Waste Management

As the volume and complexity of electronic waste grow worldwide, regional and subnational systems are increasingly tasked with managing the environmental, economic, and social challenges of circular resource recovery. This paper focuses on Canada's e‐waste sector to examine how circular economy ( ce ) principles can be integrated into regional governance frameworks. Drawing on a novel methodology with artificial intelligence (AI), natural language processing (NLP), K ‐means clustering, and theory of change (ToC) modeling, the study synthesizes over 200 academic texts to map transformation pathways toward sustainable e‐waste management. Our findings highlight gaps in national policy across provinces and the underutilized potential of community‐led and youth‐driven circular initiatives.